The Experts below are selected from a list of 114450 Experts worldwide ranked by ideXlab platform
Kenton K. Yee - One of the best experts on this subject based on the ideXlab platform.
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A Bayesian Framework for combining valuation estimates
Review of Quantitative Finance and Accounting, 2007Co-Authors: Kenton K. YeeAbstract:Discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estimates. Moreover, the valuation estimates typically disagree with market price. Can one form a superior valuation estimate by averaging over the individual estimates, including market price? This article suggests a Bayesian Framework for combining two or more estimates into a superior valuation estimate. The Framework justifies the common practice of averaging over several estimates to arrive at a final point estimate.
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A Bayesian Framework for Combining Valuation Estimates
arXiv: Statistical Finance, 2007Co-Authors: Kenton K. YeeAbstract:Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estimates. Moreover, the valuation estimates typically disagree with market price. Can one form a superior valuation estimate by averaging over the individual estimates, including market price? This article suggests a Bayesian Framework for combining two or more estimates into a superior valuation estimate. The Framework justifies the common practice of averaging over several estimates to arrive at a final point estimate.
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A Bayesian Framework for Combining Valuation Estimates
2007Co-Authors: Kenton K. YeeAbstract:Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estimates. Moreover, the valuation estimates typically disagree with market price. Can one form a superior valuation estimate by averaging over the individual estimates, including market price? This article suggests a Bayesian Framework for combining two or more estimates into a superior valuation estimate. The Framework justifies the practice -- popular on Wall Street as well as in courtrooms and tax court -- of averaging over several estimates to arrive at a final point estimate.
Dmitry Vetrov - One of the best experts on this subject based on the ideXlab platform.
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CIARP - The use of Bayesian Framework for kernel selection in vector machines classifiers
Lecture Notes in Computer Science, 2005Co-Authors: Dmitry Kropotov, Nikita Ptashko, Dmitry VetrovAbstract:In the paper we propose a method based on Bayesian Framework for selecting the best kernel function for supervised learning problem. The parameters of the kernel function are considered as model parameters and maximum evidence principle is applied for model selection. We describe a general scheme of Bayesian regularization, present model of kernel classifiers as well as our approximations for evidence estimation, and then give some results of experimental evaluation.
Jorge Dias - One of the best experts on this subject based on the ideXlab platform.
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Learning emergent behaviours for a hierarchical Bayesian Framework for active robotic perception.
Cognitive Processing, 2012Co-Authors: João Filipe Ferreira, Christiana Tsiourti, Jorge DiasAbstract:In this research work, we contribute with a behaviour learning process for a hierarchical Bayesian Framework for multimodal active perception, devised to be emergent, scalable and adaptive. This Framework is composed by models built upon a common spatial configuration for encoding perception and action that is naturally fitting for the integration of readings from multiple sensors, using a Bayesian approach devised in previous work. The proposed learning process is shown to reproduce goal-dependent human-like active perception behaviours by learning model parameters (referred to as “attentional sets”) for different free-viewing and active search tasks. Learning was performed by presenting several 3D audiovisual virtual scenarios using a head-mounted display, while logging the spatial distribution of fixations of the subject (in 2D, on left and right images, and in 3D space), data which are consequently used as the training set for the Framework. As a consequence, the hierarchical Bayesian Framework adequately implements high-level behaviour resulting from low-level interaction of simpler building blocks by using the attentional sets learned for each task, and is able to change these attentional sets “on the fly,” allowing the implementation of goal-dependent behaviours (i.e., top-down influences).
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A hierarchical Bayesian Framework for multimodal active perception
Adaptive Behavior, 2012Co-Authors: João Filipe Ferreira, Miguel Castelo-branco, Jorge DiasAbstract:In this article, we present a hierarchical Bayesian Framework for multimodal active perception, devised to be emergent, scalable and adaptive. This Framework, while not strictly neuromimetic, finds its roots in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a Bayesian approach devised in previous work. The Framework presented in this article is shown to adequately model human-like active perception behaviours, namely by exhibiting the following desirable properties: high-level behaviour results from low-level interaction of simpler building blocks; seamless integration of additional inputs is allowed by the Bayesian Programming formalism; initial 'genetic imprint' of distribution parameters may be changed 'on the fly' through parameter manipulation, thus allowing for the implementation of goal-dependent behaviours (i.e. top-down influences).
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A Bayesian Framework for Active Artificial Perception
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2012Co-Authors: Joao Ferreira, Miguel Castelo-branco, Jorge Lobo, Pierre Bessiere, Jorge DiasAbstract:In this text, we present a Bayesian Framework for active multimodal perception of 3D structure and motion. The design of this Framework finds its inspiration in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common egocentric spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a Bayesian approach. In the process, we will contribute with efficient and robust probabilistic solutions for cyclopean geometry-based stereovision and auditory perception based only on binaural cues, modelled using a consistent formalisation that allows their hierarchical use as building blocks for the multimodal sensor fusion Framework. We will explicitly or implicitly address the most important challenges of sensor fusion using this Framework, for vision, audition and vestibular sensing. Moreover, interaction and navigation requires maximal awareness of spatial surroundings, which in turn is obtained through active attentional and behavioural exploration of the environment. The computational models described in this text will support the construction of a simultaneously flexible and powerful robotic implementation of multimodal active perception to be used in real-world applications, such as human-machine interaction or mobile robot navigation.
Keiichi Tokuda - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - A model structure integration based on a Bayesian Framework for speech recognition
2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012Co-Authors: Sayaka Shiota, Kei Hashimoto, Yoshihiko Nankaku, Keiichi TokudaAbstract:This paper proposes an acoustic modeling technique based on Bayesian Framework using multiple model structures for speech recognition. The Bayesian approach is a statistical technique for estimating reliable predictive distributions by marginalizing model parameters, and its effectiveness in HMM-based speech recognition has been reported. Although the basic idea underlying the Bayesian approach is to treat all parameters as random variables, only one model structure is still selected in the conventional method. Multiple model structures are treated as latent variables in the proposed method and integrated based on the Bayesian Framework. Furthermore, we applied deterministic annealing to the training algorithm to estimate appropriate acoustic models. The proposed method effectively utilizes multiple model structures, especially in the early stage of training and this leads to better predictive distributions and improvement of recognition performance.
Rosario M. Balboa - One of the best experts on this subject based on the ideXlab platform.
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A Bayesian Framework for sensory adaptation
Neural computation, 2002Co-Authors: Norberto M. Grzywacz, Rosario M. BalboaAbstract:Adaptation allows biological sensory systems to adjust to variations in the environment and thus to deal better with them. In this article, we propose a general Framework of sensory adaptation. The underlying principle of this Framework is the setting of internal parameters of the system such that certain prespecified tasks can be performed optimally. Because sensorial inputs vary probabilistically with time and biological mechanisms have noise, the tasks could be performed incorrectly. We postulate that the goal of adaptation is to minimize the number of task errors. This minimization requires prior knowledge of the environment and of the limitations of the mechanisms processing the information. Because these processes are probabilistic, we formulate the minimization with a Bayesian approach. Application of this Bayesian Framework to the retina is successful in accounting for a host of experimental findings.
